




'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    int      ,		a_						:    str=""      ,		a_						:    List[Any]="train"							):



						"""simple docstring"""




						assert os.path.isdir(a_							)

						__snake_case			=		[]
						__snake_case			=		os.listdir(a_							)
						for story_filename in story_filenames_list:
									if "summary" in story_filename:
												continue
									__snake_case			=		os.path.join(a_      ,		a_							)
									if not os.path.isfile(a_							):
												continue
									self.documents.append(a_							)





			def __len__(      self						:    int							):



						"""simple docstring"""




						return len(self.documents							)






			def __getitem__(      self						:    List[Any]      ,		a_						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		self.documents[idx]
						__snake_case			=		document_path.split("/"							)[-1]
						with open(a_      ,		encoding="utf-8"							) as source:
									__snake_case			=		source.read()
									__snake_case		,     __snake_case			=		process_story(a_							)
						return document_name, story_lines, summary_lines







def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      List[str]:
			__snake_case			=		list(filter(lambda _UpperCAmelCase						: len(_UpperCAmelCase						) != 0		,				[line.strip() for line in raw_story.split("\n"						)]						)						)

			# for some unknown reason some lines miss a period, add it
			__snake_case			=		[_add_missing_period(_UpperCAmelCase						) for line in nonempty_lines]

			# gather article lines
			__snake_case			=		[]
			__snake_case			=		deque(_UpperCAmelCase						)
			while True:
						try:
									__snake_case			=		lines.popleft()
									if element.startswith("@highlight"						):
												break
									story_lines.append(_UpperCAmelCase						)
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			__snake_case			=		list(filter(lambda _UpperCAmelCase						: not t.startswith("@highlight"						)		,				_UpperCAmelCase						)						)

			return story_lines, summary_lines


def __UpperCAmelCase	(   _UpperCAmelCase		:   Tuple						)      ->      Tuple:
			__snake_case			=		[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
			if line.startswith("@highlight"						):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."


def __UpperCAmelCase	(   _UpperCAmelCase		:   Tuple		,				_UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Dict						)      ->      Dict:
			if len(_UpperCAmelCase						) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(_UpperCAmelCase						))						)
						return sequence


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[Any]		,				_UpperCAmelCase		:   Any						)      ->      Dict:
			__snake_case			=		torch.ones_like(_UpperCAmelCase						)
			__snake_case			=		sequence == pad_token_id
			__snake_case			=		0
			return mask


def __UpperCAmelCase	(   _UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   str		,				_UpperCAmelCase		:   int						)      ->      str:
			__snake_case			=		[tokenizer.encode(_UpperCAmelCase						) for line in story_lines]
			__snake_case			=		[token for sentence in story_lines_token_ids for token in sentence]
			__snake_case			=		[tokenizer.encode(_UpperCAmelCase						) for line in summary_lines]
			__snake_case			=		[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids






def __UpperCAmelCase	(   _UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   Union[str, Any]						)      ->      List[Any]:
			__snake_case			=		[]
			for sequence in batch:
						__snake_case			=		-1
						__snake_case			=		[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2						)
						batch_embeddings.append(_UpperCAmelCase						)
			return torch.tensor(_UpperCAmelCase						)







def 							A__       (    lowerCamelCase = 4_00_00_00    )	->						int:
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    0, 1
				while b <= n:
								if b % 2 == 0:
												even_fibs.append(lowerCamelCase    )
								UpperCamelCase_, UpperCamelCase_:				int						=    b, a + b
				return sum(lowerCamelCase    )


if __name__ == "__main__":
							print(F"""{solution() = }""")






